{"id":"https://openalex.org/W1497188095","doi":"https://doi.org/10.1109/tifs.2015.2434320","title":"Generalizing DET Curves Across Application Scenarios","display_name":"Generalizing DET Curves Across Application Scenarios","publication_year":2015,"publication_date":"2015-05-18","ids":{"openalex":"https://openalex.org/W1497188095","doi":"https://doi.org/10.1109/tifs.2015.2434320","mag":"1497188095"},"language":"en","primary_location":{"id":"doi:10.1109/tifs.2015.2434320","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tifs.2015.2434320","pdf_url":null,"source":{"id":"https://openalex.org/S61310614","display_name":"IEEE Transactions on Information Forensics and Security","issn_l":"1556-6013","issn":["1556-6013","1556-6021"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Information Forensics and Security","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5029657515","display_name":"Norman Poh","orcid":"https://orcid.org/0000-0001-6076-0309"},"institutions":[{"id":"https://openalex.org/I28290843","display_name":"University of Surrey","ror":"https://ror.org/00ks66431","country_code":"GB","type":"education","lineage":["https://openalex.org/I28290843"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Norman Poh","raw_affiliation_strings":["University of Surrey, Guildford, U.K","[University of Surrey, Guildford, U.K]"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Surrey, Guildford, U.K","institution_ids":["https://openalex.org/I28290843"]},{"raw_affiliation_string":"[University of Surrey, Guildford, U.K]","institution_ids":["https://openalex.org/I28290843"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5073481748","display_name":"Chi Ho Chan","orcid":"https://orcid.org/0000-0002-3560-0140"},"institutions":[{"id":"https://openalex.org/I28290843","display_name":"University of Surrey","ror":"https://ror.org/00ks66431","country_code":"GB","type":"education","lineage":["https://openalex.org/I28290843"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Chi Ho Chan","raw_affiliation_strings":["University of Surrey, Guildford, U.K","[University of Surrey, Guildford, U.K]"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Surrey, Guildford, U.K","institution_ids":["https://openalex.org/I28290843"]},{"raw_affiliation_string":"[University of Surrey, Guildford, U.K]","institution_ids":["https://openalex.org/I28290843"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I28290843"],"apc_list":null,"apc_paid":null,"fwci":0.4365,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.75195239,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"10","issue":"10","first_page":"2171","last_page":"2181"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9969000220298767,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9969000220298767,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11800","display_name":"User Authentication and Security Systems","score":0.9934999942779541,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11598","display_name":"Internet Traffic Analysis and Secure E-voting","score":0.9781000018119812,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/biometrics","display_name":"Biometrics","score":0.9178941249847412},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8133527636528015},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.5572258234024048},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.5379377603530884},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5076972246170044},{"id":"https://openalex.org/keywords/outcome","display_name":"Outcome (game theory)","score":0.4711662232875824},{"id":"https://openalex.org/keywords/operator","display_name":"Operator (biology)","score":0.4562060534954071},{"id":"https://openalex.org/keywords/receiver-operating-characteristic","display_name":"Receiver operating characteristic","score":0.45275190472602844},{"id":"https://openalex.org/keywords/demographics","display_name":"Demographics","score":0.4307232201099396},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4299185276031494},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.42457136511802673},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.39347130060195923},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3344799280166626},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.222537100315094},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13828736543655396}],"concepts":[{"id":"https://openalex.org/C184297639","wikidata":"https://www.wikidata.org/wiki/Q177765","display_name":"Biometrics","level":2,"score":0.9178941249847412},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8133527636528015},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.5572258234024048},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.5379377603530884},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5076972246170044},{"id":"https://openalex.org/C148220186","wikidata":"https://www.wikidata.org/wiki/Q7111912","display_name":"Outcome (game theory)","level":2,"score":0.4711662232875824},{"id":"https://openalex.org/C17020691","wikidata":"https://www.wikidata.org/wiki/Q139677","display_name":"Operator (biology)","level":5,"score":0.4562060534954071},{"id":"https://openalex.org/C58471807","wikidata":"https://www.wikidata.org/wiki/Q327120","display_name":"Receiver operating characteristic","level":2,"score":0.45275190472602844},{"id":"https://openalex.org/C2780084366","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demographics","level":2,"score":0.4307232201099396},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4299185276031494},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42457136511802673},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39347130060195923},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3344799280166626},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.222537100315094},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13828736543655396},{"id":"https://openalex.org/C158448853","wikidata":"https://www.wikidata.org/wiki/Q425218","display_name":"Repressor","level":4,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C144237770","wikidata":"https://www.wikidata.org/wiki/Q747534","display_name":"Mathematical economics","level":1,"score":0.0},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C86339819","wikidata":"https://www.wikidata.org/wiki/Q407384","display_name":"Transcription factor","level":3,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/tifs.2015.2434320","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tifs.2015.2434320","pdf_url":null,"source":{"id":"https://openalex.org/S61310614","display_name":"IEEE Transactions on Information Forensics and Security","issn_l":"1556-6013","issn":["1556-6013","1556-6021"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Information Forensics and Security","raw_type":"journal-article"},{"id":"pmh:oai:alma.44SUR_INST:11139603030002346","is_oa":false,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4210197018","display_name":"View","issn_l":"2688-268X","issn":["2688-268X","2688-3988"],"is_oa":false,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320595","host_organization_name":"Wiley","host_organization_lineage":["https://openalex.org/P4310320595"],"host_organization_lineage_names":["Wiley"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"","raw_type":""},{"id":"pmh:oai:epubs.surrey.ac.uk:812520","is_oa":false,"landing_page_url":"http://epubs.surrey.ac.uk/812520/1/Generalized_DET_curves_with_factors15.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400680","display_name":"Surrey Research Insight Open Access (The University of Surrey)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I28290843","host_organization_name":"University of Surrey","host_organization_lineage":["https://openalex.org/I28290843"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W28384670","https://openalex.org/W155989358","https://openalex.org/W1500619482","https://openalex.org/W1523571582","https://openalex.org/W1584545576","https://openalex.org/W2008733468","https://openalex.org/W2016622881","https://openalex.org/W2040262929","https://openalex.org/W2057335587","https://openalex.org/W2126963242","https://openalex.org/W2167806820","https://openalex.org/W2307513746","https://openalex.org/W2476229988","https://openalex.org/W4237828103","https://openalex.org/W4252852428","https://openalex.org/W6654612037"],"related_works":["https://openalex.org/W3121380072","https://openalex.org/W2058403539","https://openalex.org/W2076845124","https://openalex.org/W2183964146","https://openalex.org/W2062586268","https://openalex.org/W2379932303","https://openalex.org/W2942793592","https://openalex.org/W2018278180","https://openalex.org/W3124636075","https://openalex.org/W3132695196"],"abstract_inverted_index":{"Assessing":[0],"biometric":[1,27,60,86,108],"performance":[2,56,87,155],"is":[3,43,74,81,192,199],"challenging":[4],"because":[5],"an":[6,21,78],"experimental":[7],"outcome":[8],"depends":[9],"on":[10,169,183],"the":[11,16,38,71,102,113,120,123,134,142,145,153,170,179],"choice":[12],"of":[13,20,40,58,83,91,115,136,144,172,181,190],"demographics":[14],"and":[15,32,107,161,164,196],"chosen":[17],"application":[18,67],"scenario":[19],"experiment.":[22],"If":[23],"one":[24,36],"can":[25,131],"quantify":[26],"samples":[28],"into":[29],"good,":[30],"bad,":[31],"ugly":[33],"categories":[34,42],"for":[35,48,122],"application,":[37],"proportion":[39,114],"these":[41],"likely":[44],"to":[45,64,110,125],"be":[46],"different":[47,66,127],"another":[49,65],"application.":[50,139],"As":[51],"a":[52,54,59,85,137],"result,":[53],"typical":[55],"curve":[57,88],"experiment":[61],"cannot":[62],"generalize":[63],"setting,":[68],"even":[69],"though":[70],"same":[72],"system":[73,154],"used.":[75],"We":[76,140],"propose":[77],"algorithm":[79,146],"that":[80,130,178,204],"capable":[82],"generalizing":[84],"in":[89,147,188],"terms":[90,189],"detection":[92],"error":[93],"tradeoff":[94],"or":[95],"equivalently":[96],"receiver's":[97],"operating":[98,128],"characteristics,":[99],"by":[100],"allowing":[101],"user":[103,124],"(system":[104],"operator,":[105],"policy-maker,":[106],"researcher)":[109],"explicitly":[111],"set":[112],"data":[116],"differently.":[117],"This":[118],"offers":[119],"possibility":[121],"simulate":[126],"conditions":[129],"better":[132,201],"match":[133],"setting":[135],"target":[138],"demonstrated":[141],"utility":[143],"three":[148],"scenarios,":[149],"namely:":[150],"1)":[151],"estimating":[152],"under":[156],"varying":[157],"quality;":[158],"2)":[159],"spoof":[160],"zero-effort":[162],"attacks;":[163],"3)":[165],"cross-device":[166],"matching.":[167],"Based":[168],"results":[171],"1300":[173],"use-case":[174],"experiments,":[175],"we":[176],"found":[177],"quality":[180],"prediction":[182],"unseen":[184],"(test)":[185],"data,":[186],"measured":[187],"coverage,":[191],"typically":[193],"between":[194],"60%":[195],"80%,":[197],"which":[198],"significantly":[200],"than":[202],"random,":[203],"is,":[205],"50%.":[206]},"counts_by_year":[{"year":2019,"cited_by_count":1},{"year":2015,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}